Python AI Developer

TekWissen•Bellevue, WA

4d•Onsite

About The Position

We're looking for a mid-level Python Developer with hands-on AI/ML experience to design, build, and deploy AI-powered applications and services. You'll work across the stack — from data pipelines and model integration to production APIs while also using AI coding assistants as a core part of your daily engineering workflow.

Requirements

3–6 years of professional Python development experience
Strong proficiency in Python 3.x and at least one web framework (FastAPI, Flask, or Django)
Hands-on experience building and integrating LLM/AI applications (chatbots, RAG, summarization, classification, or agentic systems)
Experience with at least one ML/AI framework: LangChain, LlamaIndex, Hugging Face, PyTorch, or TensorFlow
Experience with RESTful API design and microservices architecture
Solid understanding of SQL and database design
Practical, hands-on experience using AI coding assistants (Copilot, Claude Code, Cursor, etc.) in a real development workflow
Familiarity with unit/integration testing frameworks (pytest, unittest)
Working knowledge of Git, CI/CD pipelines, and containerization (Docker)

Nice To Haves

Experience with vector databases and embedding models
Familiarity with prompt engineering, LLM evaluation, and guardrails/safety practices
Exposure to cloud platforms (AWS, Azure, or GCP) and their AI/ML services (SageMaker, Vertex AI, Azure ML)
Experience with Kafka or similar event-streaming platforms
Understanding of MCP (Model Context Protocol) or agentic tool-calling frameworks
Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow)
Prior experience in Agile/Scrum environments
Contributions to internal AI tooling adoption or engineering productivity initiatives
Experience with observability tools (Datadog, Splunk, Grafana)
Familiarity with data engineering tools (Airflow, Spark, dbt)

Responsibilities

Design, build, and maintain Python services and APIs (FastAPI, Flask, or Django)
Write clean, well-tested, maintainable code following established engineering standards
Participate in code reviews, design discussions, and sprint planning
Debug and resolve production issues, including performance tuning and root cause analysis
Work with relational and/or NoSQL databases (PostgreSQL, MySQL, MongoDB, Redis)
Build and consume REST/gRPC APIs; work with message queues (Kafka, RabbitMQ, SQS) and async task frameworks (Celery)
Build and deploy ML/LLM-based services using Python, integrating models via APIs (OpenAI, Anthropic) or self-hosted inference
Design and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases (pgvector, Pinecone, Weaviate, FAISS)
Develop agentic workflows and tool-calling integrations connecting LLMs to internal APIs and data sources
Use frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, or PyTorch for model development and orchestration
Fine-tune, evaluate, and monitor model performance; implement prompt engineering and evaluation harnesses
Collaborate with Data Science/ML teams on feature engineering, model serving infrastructure, and MLOps practices
Apply sound judgment on when/where AI capabilities add real product value vs. added complexity
Use AI coding assistants (GitHub Copilot, Claude Code, Cursor, or similar) to accelerate development, refactoring, and debugging
Apply AI-assisted test generation and code review practices to improve velocity without sacrificing quality
Continuously evaluate and adopt emerging AI-augmented engineering tools and workflows
Mentor peers on effective, responsible use of AI tools in the SDLC